There is an increasing trend of packaging and sharing tools in the epidemiology research community. But these tools remain difficult to use and to integrate in a data analysis pipeline. There is a need for a more integrated approach, ensuring that the various tools work well with one another. For example, minimal data wrangling should be needed to transform the output of one tool before passing it to the next tool down the data analysis pipeline. Similarly, various alternatives for a single step of the pipeline should as much as possible use the same inputs and return the same outputs. In this talk, I will present how the Epiverse-TRACE project collaborates with many R package developers in epidemiology to integrate their tools in a unified universe. Indeed, the unique and challenging feature of Epiverse is that it doesn’t intend to create a unified universe from scratch, but instead aims at updating existing external pieces of software to better work together. This talk will explain how we identify the key parts that should be updated, and how we make these updates with minimal disruption to the individual package developers and established community of users.